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Update nx-cugraph Docs URLs #4761

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Update nx-cugraph Docs URLs #4761

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4 changes: 2 additions & 2 deletions docs/cugraph/source/nx_cugraph/benchmarks.md
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Expand Up @@ -9,7 +9,7 @@ We ran several commonly used graph algorithms on both `networkx` and `nx-cugraph
![bench-image](../_static/bc_benchmark.png)

<figcaption style="text-align: center;">Results from running this <a
href="https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based/bench_algos.py">Benchmark</a><span
href="https://github.com/rapidsai/nx-cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based/bench_algos.py">Benchmark</a><span
class="title-ref"></span></figcaption>
</figure>

Expand All @@ -23,4 +23,4 @@ Below are the steps to reproduce the results on your own.

4. Install the latest `nx-cugraph` by following the [Installation Guide](installation.md)

5. Follow the instructions written in the README [here](https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based)
5. Follow the instructions written in the README [here](https://github.com/rapidsai/nx-cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based/README.md)
2 changes: 1 addition & 1 deletion docs/cugraph/source/nx_cugraph/how-it-works.md
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Expand Up @@ -110,4 +110,4 @@ This run will be much faster, typically around 5 seconds depending on your GPU.

---

The latest list of algorithms supported by `nx-cugraph` can be found in [GitHub](https://github.com/rapidsai/cugraph/blob/HEAD/python/nx-cugraph/README.md#algorithms), or in the [Supported Algorithms Section](supported-algorithms.md).
The latest list of algorithms supported by `nx-cugraph` can be found in [GitHub](https://github.com/rapidsai/nx-cugraph/blob/HEAD/README.md#supported-algorithms), or in the [Supported Algorithms Section](supported-algorithms.md).
2 changes: 1 addition & 1 deletion docs/cugraph/source/nx_cugraph/index.rst
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Expand Up @@ -3,7 +3,7 @@ nx-cugraph

``nx-cugraph`` is a NetworkX backend that provides **GPU acceleration** to many popular NetworkX algorithms.

By simply `installing and enabling nx-cugraph <https://github.com/rapidsai/cugraph/blob/HEAD/python/nx-cugraph/README.md#install>`_, users can see significant speedup on workflows where performance is hindered by the default NetworkX implementation.
By simply `installing and enabling nx-cugraph <https://github.com/rapidsai/nx-cugraph/blob/HEAD/README.md#installation>`_, users can see significant speedup on workflows where performance is hindered by the default NetworkX implementation.
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I would reference the docs, which is what I did in #4756

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By simply `installing and enabling nx-cugraph <https://github.com/rapidsai/nx-cugraph/blob/HEAD/README.md#installation>`_, users can see significant speedup on workflows where performance is hindered by the default NetworkX implementation.
By simply `installing and enabling nx-cugraph <https://docs.rapids.ai/api/cugraph/stable/nx_cugraph/installation/>`_, users can see significant speedup on workflows where performance is hindered by the default NetworkX implementation.


Users can have GPU-based, large-scale performance **without** changing their familiar and easy-to-use NetworkX code.

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2 changes: 1 addition & 1 deletion docs/cugraph/source/nx_cugraph/supported-algorithms.rst
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Expand Up @@ -352,4 +352,4 @@ Generators


To request nx-cugraph backend support for a NetworkX API that is not listed
above, visit the `cuGraph GitHub repo <https://github.com/rapidsai/cugraph>`_.
above, visit the `nx-cugraph GitHub repo <https://github.com/rapidsai/nx-cugraph>`_.
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